One of them is the work reported in (Chung and Gildea, 2010), where three approaches are compared, based on either pattern matching, CRF, or parsing.

there is no comparison between using gold trees and automatic trees.


It seems performing parsing and tagging with the bilingually-induced POS tagset is too difficult when only monolingual in-formation is available to the parser.

our bilingually-induced POSs, except for Joint [P ], with the lower accuracies are more effective for SMT than the monolingually-induced POSs and the original POSs, as indicated in Table 1.


Methods that have proven successful for paraphrase detection (Deerwester et al., 1990;Dolan et al., 2004), as in the main clauses of b and c, include latent variable models that simultaneously capture the semantics of words and sentences, such as latent semantic analysis (LSA) or latent Dirichlet allocation (LDA).

our task goes beyond paraphrase detection.


The kernel computational complexity is , where all pairwise comparisons are carried out between T 1 and T 2 .

there are fast algorithms for kernel computation that run in linear time on average, either by dynamic programming (Collins and Duffy, 2002), or pre-sorting production rules before training (Moschitti, 2006).


Our evaluation relies on the Matthews correlation coefficient (MCC, also known as the φcoefficient) (Matthews, 1975) to avoid the bias of accuracy due to data skew, and to produce a robust summary score independent of whether the positive class is skewed to the majority or minority.

to f-measure, which is a classspecific weighted average of precision and recall, and whose weighted version depends on a choice of whether the class-specific weights should come from the training or testing data, MCC is a single summary value that incorporates all 4 cells of a 2 × 2 confusion matrix (TP, FP, TN and FN for True or False Positive or Negative).


As for classes or senses, it may not be a common assumption.

when the classes for all-words WSD are enormous, finegrained, and can be associated with distance, we can rather naturally assume the continuity also for senses.


The cluster centers are located at the means of hypotheses including miscellaneous alternatives not intended, thus the estimated probability distribution is, roughly speaking, offset toward the center of WordNet, which is not what we want.

the proposed method proceeds to Figure 2(c) and finds clusters in the data after conflicting data is erased.


some structures like sequences or parse trees, specialized and tractable dynamic programming algorithms have proven to be very effective.

as the structures under consideration become increasingly complex, the computational problem of predicting structures can become very expensive, and in the worst case, intractable.


We choose this baseline because it is shown to give the highest improvement in wall-clock time and also in terms of the number of cache hits.

we note that the results presented in our work outperform all the previous amortization algorithms, including the approximate inference methods.


Lagrangian Relaxation in the literature In the literature, in applications of the Lagrangian relaxation technique (such as (Rush and Collins, 2011;Chang and Collins, 2011;Reichart and Barzilay, 2012) and others), the relaxed problems are solved using specialized algorithms.

in both the relaxations considered in this paper, even the relaxed problems cannot be solved without an ILP solver, and yet we can see improvements from decomposition in Table 1.


In general, a tree has width one, and it can be shown that a graph has treewidth at most two iff it does not have the following graph as a minor (Bodlaender, 1997): Finding a tree decomposition with minimal width is in general NP-hard (Arnborg et al., 1987).

we find that for the graphs we are interested in in NLP applications, even a naïve algorithm gives tree decompositions of low width in practice: simply perform a depth-first traversal of the edges of the graph, forming a tree T .


In general, however, such supervision is not always available or easy to obtain.

databases are often abundantly available, especially for important domains.


Results suggested that 3-year-old children are capable of deception, and that non-verbal behaviors during deception include increases in 'positive' behaviors (e.g., smiling).

verbal cues of deception were not analyzed.


Ideally, we would like to have at our disposal a large annotated training set for our new concept of sentiment relevance.


Our work is most closely related to (Taboada et al., 2009) who define a fine-grained classification that is similar to sentiment relevance on the highest level.

unlike our study, they fail to experimentally compare their classification scheme to prior work in their experiments and to show that this scheme is different.


In addition, they work on the paragraph level.

paragraphs often contain a mix of S-relevant and S-nonrelevant sentences.


The best model (line 7) performs better than MinCut (3) by 3.1% and better than training on purely rule-generated DS labels (line 2) by 5.8%.

we did not find a cumulative effect (line 8) of the two feature sets.


The optimization criterion does not always correlate perfectly with F 1 .

we find no statistically significant difference between the selected result and the highest F 1 value.


To date, the modeling of emotion in a dialogue has extensively been studied in NLP as well as related areas (Forbes-Riley and Litman, 2004;Ayadi et al., 2011).

the past attempts are virtually restricted to estimating the emotion of an addresser 1 from her/his utterance.


In this case, the first two utterances are used to learn the translation model, while only the second utterance is used to learn the language model.

this simple approach is prone to suffer from the data sparseness problem.


For all the four features (i.e., two phrase translation probabilities and two lexical weights) derived from translation model, the weights of the adapted model are equally set as α (0 ≤ α ≤ 1.0).

we use SRILM for the interpolation of language models.


We can clearly observe the improvement in the BLEU from 0.64 to 1.05.

there still remains a gap between the last two rows (i.e., proposed and optimal).


As the Figures show, in both tasks, SHEM achieved high performances with 11 emotions.

bHEM achieved high performances with six emotions.


For example, a threshold like 0.3 or 0.4 improves the performance.

if a large value (e.g., 0.6) is selected as threshold, the performance decreases.


They also utilized Latent Semantic Analysis (LSA) (Landauer et al., 1998) as another semantic similarity measure.

both PMI and LSA are semantic similarity measure.


We assumed that the type and number of emotions are pre-defined and our approach was based on this assumption.

in previous research, there is little agreement about the number and types of basic emotions.


Still, we assume that the different users will produce information of varying quality, and some should be eliminated entirely.

we emphasise that there may be smaller 1 Data collection was performed using Twitter API, http://dev.twitter.com/, to extract all posts for our target users.


As mentioned before, one could also try the opposite (i.e., start by expanding the user space); both those models can also be optimised in an iterative process.

our experiments revealed that those approaches did not improve on the performance of BEN.


For the UK case study, both BEN and BGL are able to beat all baselines in average performance across all parties.

in the Austrian case study, LEN performs better that BEN, something that could be justified by the fact that the users in C au were selected by domain experts, and consequently there was not much gain to be had by filtering them further.


First, using the fact that Now the problem is equivalent to: This is equivalent to the ILP:  In the previous section, we assume that n b,ref is at hand (reference abstractive summary is given) and propose a bigram-based optimization framework for extractive summarization.

for the summarization task, the bigram frequency is unknown, and thus our first goal is to estimate such frequency.


On one hand, this is to save computational cost for the ILP module.

we see from the table that only 127 of these more than 2K bigrams are in the reference summary and are thus expected to help the summary responsiveness.


We can see that the ICSI ILP system performs better when the input bigrams have less noise (those bigrams that are not in summary).

our proposed method is slightly more robust to this kind of noise, possibly because of the weights we use in our system -the noisy bigrams have lower weights and thus less impact on the final system performance.


This is because their system was tuned for the particular summarization task using the DUC 2003 corpus.

even without any parameter tuning our method yields good performance, as evidenced by results on the two different summarization tasks.


Joint models of sentence extraction and compression have a great benefit in that they have a large degree of freedom as far as controlling redundancy goes.

conventional two-stage approaches (Zajic et al., 2006), which first generate candidate compressed sentences and then use them to generate a summary, have less computational complexity than joint models.


That is, we extract subsentences for making the summary directly from all available subsentences in the documents and not in a stepwise fashion.

there is a difficulty with such a formalization.


V is partitioned into exclusive subsets B of valid subtrees, and each subset corresponds to the original sentence from which the valid subtrees derived.

the cost of a union of subtrees from different sentences is simply the sum of the costs of subtrees, while the cost of a union of subtrees from the same sentence is smaller than the sum of the costs.


In this joint model, we generate a compressed sentence by extracting an arbitrary subtree from a dependency tree of a sentence.


Comparing the results of SbE and RC, we can see that the sentence compression caused the recall of SbE to be 7% lower than that of RC.

the drop is relatively small in light of the fact that the sentence compression can discard 19% of the original character length with SbE.


We show that for the special case of linear PCFGs (which include HMMs) with non-degenerate priors the posterior puts zero mass on non-tight PCFGs, so tightness is not an issue with Bayesian estimation of such grammars.

because all of the commonly used priors (such as the Dirichlet or the logistic normal) assign non-zero probability across the whole probability simplex, in general the posterior may assign non-zero probability to nontight PCFGs.


This dominance is mainly because chunkbased dependency analysis looks most appropriate for Japanese syntax due to its morphosyntactic typology, which includes agglutination and scrambling (Bekki, 2010).

it is also true that this type of analysis has prevented us from deeper syntactic analysis such as deep parsing (Clark and Curran, 2007) and logical inference (Bos et al., 2004;Bos, 2007), both of which have been surpassing shallow parsing-based approaches in languages like English.


13, RelIn is assigned because the right NP "book" is annotated as an accusative argument of the predicate "buy".

relExt is assigned in the lower side in the figure because the right NP "store" is not annotated as an argument.


), an unsupervised model usually suffers from two drawbacks, i.e., lower performance and higher computational cost.

bilingual projection (Hwa et al., 2005;Smith and Eisner, 2009;Jiang and Liu, 2010) seems a promising substitute for languages with a large amount of bilingual sentences and an existing parser of the counterpart language.


Most commonly used alignment models, such as the IBM models and HMM-based aligner are unsupervised learners, and can only capture simple distortion features and lexical translational features due to the high complexity of the structure prediction space.

the CRFbased NER models are trained on manually annotated data, and admit richer sequence and lexical features.


Morph can be considered as a special case of alias used for hiding true entities in malicious environment (Hsiung et al., 2005;Pantel, 2006).

social network plays an important role in generating morphs.


Furthermore, our approaches can also be applied for satire or other implicit meaning recognition, as well as information extraction (Bollegala et al., 2011).

morph resolution in social media is challenging due to the following reasons.


Given the constructed network, a straightforward solution for finding the target for a morph is to use link-based similarity search.

now objects are linked to different types of neighbors, if all neighbors are treated as the same, it may cause information loss problems.


For example, using only surface features, the real target "乔布斯 (Steve Jobs)" of the morph "乔帮 主 (Qiao Boss)" is not top ranked since some other candidates such as "乔治 (George)" are more orthographically similar.

"Steve Jobs" is ranked top when combined with semantic features.


Translations get propagated to oov nodes using a label propagation technique.

beside the difference in the oov label assignment, there is a major difference between our bipartite graph and the baseline (Marton et al., 2009): we do not use a heuristic to reduce the number of neighbor candidates and we consider all possible candidates that share at least one context word.


A common practice to control the number of edges is to connect each node to at most k other nodes (k-nearest neigh-bor).

finding the top-k nearest nodes to each node requires considering its similarity to all the other nodes which requires O(n 2 ) computations and since n is usually very large, doing such is practically intractable.


The correctness of this gold standard is limited to the size of the parallel data used as well as the quality of the word alignment software toolkit, and is not 100% precise.

it gives a good estimate of how each oov should be translated without the need for human judgments.


Most have focused on extracting a translation lexicon by mining monolingual resources of data to find clues, using probabilistic methods to map words, or by exploiting the cross-language evidence of closely related languages.

most of them evaluated only highfrequency words of specific types (nouns or content words) (Rapp, 1995;Koehn and Knight, 2002;Haghighi et al., 2008;Garera et al., 2009;Laws et al., 2010) we do not consider any constraint on our test data and our data includes many low frequency words.


Predicate-argument structure (PAS) has been demonstrated to be very effective in improving SMT performance.

since a sourceside PAS might correspond to multiple different target-side PASs, there usually exist many PAS ambiguities during translation.


Considering that current syntaxbased translation models are always impaired by cross-lingual structure divergence (Eisner, 2003;, PAS is really a better representation of a sentence pair to model the bilingual structure mapping.

since a source-side PAS might correspond to multiple different target-side PASs, there usually exist many PAS ambiguities during translation.


From Figure 1, we can see that (a) and (c) get the same source-side PAS and target-side-like PAS.

they are different because in Figure 1(c), there is a gap string "对 运动员" between [A0] and [Pred].


Researchers such as Swan and Smith (2001), Aarts and Granger (1998), Davidsen-Nielsen and Harder (2001), and Altenberg and Tapper (1998) work on mother tongue interference to reveal overused/underused words, part of speech (POS), or grammatical items.

very little is known about how strongly mother tongue interference is transferred to another language and about what relation there is across mother tongues.


For example, Slavic languages have a rich inflectional case system (e.g., Czech has seven inflectional cases) whereas French does not.

the difference in the richness cannot be transferred into English because English has almost no inflectional case system.


In contrast, the Slavic Englishes are scattered.

the ratios give a clue to how to distinguish Slavic Englishes from the others when combined with other


Thus, co-occurrence of words in n-word windows, syntactic structures, sentences, paragraphs, and even whole documents is captured in vector-space models built from text corpora (Turney and Pantel, 2010;Basili and Pennacchiotti, 2010;Erk and Padó, 2008;Mitchell and Lapata, 2008;Bullinaria and Levy, 2007;Jones and Mewhort, 2007;Pado and Lapata, 2007;Lin, 1998;Landauer and Dumais, 1997;Lund and Burgess, 1996;Salton et al., 1975).

little is known about typical profiles of texts in terms of co-occurrence behavior of their words.


Thus, inasmuch as highest PMI values tend to capture multi-word expressions (South and Africa; Merill and Lynch), morphological variants (bids and bidding), or synonyms (mergers and takeovers), their proportion in word type pairs does not seem to give a clear signal regarding the quality of writing.

8 the area of moderately high PMI values (from PMI=2.5 to PMI=3.67 in Figure 2) produces a very consistent picture, with only two points out of 48 in that interval 9 lacking significant positive correlation with essay score (p2 at PMI=3.17 and p5 at PMI=3).


Because the style of informal writing may be different in different data sources, tailoring an approach towards a particular data source can improve performance in the desired domain.

this is often done at the cost of adaptability.


Unlike on previous datasets, the use of generic mappings only provided a small improvement over the baseline.

the use of domain-specific generators once again led to significantly increased performance on subjects and objects.


These systems limited their searchspace to the elements that share a syntactical relation with the predicate.

when the participants of a predicate are implicit this approach obtains incomplete predicative structures with null arguments.


For the predicate plan in the previous sentence, a traditional SRL process only returns the filler for the argument arg1, the theme of the plan.

in both examples, a reader could easily infer the missing arguments from the surrounding context of the predicate, and determine that in (1) both instances of the predicate share the same arguments and in (2) the missing argument corresponds to the subject of the verb that dominates the predicate, Quest Medical Inc. Obviously, this additional annotations could contribute positively to its semantic analysis.


We found many derivational patterns in German to be conceptually simple (e.g., verb-noun zero derivation) so that substantial coverage can already be achieved with very simple transformation functions.

there are many more complex patterns (e.g., suffixation combined with optional stem changes) that in sum also affect a considerable number of lemmas, which required us to either implement low-coverage rules or generalize existing rules.


Both the developers of these systems as well as researchers working on the subject matter frequently claim their approaches to be searching the entire web or, at least, to be scalable to web size.

there is hardly any evidence to substantiate this claim-rather the opposite can be observed: commercial plagiarism detectors have not been found to reliably identify plagiarism from the web (Köhler and Weber-Wulff, 2010), and the evaluation of research prototypes even under laboratory conditions shows that there is still a long way to go (Potthast et al., 2010b).


These relations are extracted from unstructured or semi-structured text using ontology learning from scratch (Velardi et al., 2013) and Open Information Extraction techniques (Etzioni et al., 2005;Yates et al., 2007;Wu and Weld, 2010;Fader et al., 2011;Moro and Navigli, 2013) which mainly stem from seminal work on is-a relation acquisition (Hearst, 1992) and subsequent developments (Girju et al., 2003;Pasca, 2004;Snow et al., 2004, among others).

these knowledge resources still lack semantic information about language units such as phrases and collocations.


So far the arguments in D π have been semantically annotated with the Wikipedia pages they refer to.

using Wikipedia as our sense inventory is not desirable; in fact, contrarily to other commonly used lexical-semantic networks such as WordNet, Wikipedia is not formally organized in a structured, taxonomic hierarchy.


As a result of the probabilistic association of each semantic class c with a target lexical predicate Once the semantic predicates for the input lexical predicate π have been learned, we can classify a new filling argument a of π.

the class probabilities calculated with Formula 1 might not provide reliable scores for several classes, including unseen ones whose probability would be 0.


The availability of Web-scale corpora has led to the production of large resources of relations (Etzioni et al., 2005;Yates et al., 2007;Wu and Weld, 2010;Carlson et al., 2010;Fader et al., 2011).

these resources often operate purely at the lexical level, providing no information on the semantics of their arguments or relations.


As a result, alternative approaches have been proposed that eschew taxonomies in favor of rating the quality of potential relation arguments (Erk, 2007;Chambers and Jurafsky, 2010) or generating probability distributions over the arguments (Rooth et al., 1999;Pantel et al., 2007;Bergsma et al., 2008;Ritter et al., 2010;Séaghdha, 2010;Bouma, 2010;Jang and Mostow, 2012) in order to obtain higher coverage of preferences.

we overcome the data sparsity of class-based models by leveraging the large quantity of collaboratively-annotated Wikipedia text in order to connect predicate arguments with their semantic class in WordNet using BabelNet (Navigli and Ponzetto, 2012); because we map directly to WordNet synsets, we provide a more readilyinterpretable collocation preference model than most similarity-based or probabilistic models.


We will show that they cannot be reconstructed solely from the source text, extending Copeck and Szpakowicz (2004)'s result to caseframes.

we also show that if articles from the same domain are added, reconstruction then becomes possible.


The introduction of nonterminals in the SCFG rules provides some degree of generalization.

as mentioned earlier, the context-free assumption ingrained in the syntax-based formalism often limits the model's ability to influence global reordering decision that involves cross-boundary contexts.


A straightforward approach for this is to use a supervised aligner to align the words in the sentences and then derive the reference reordering as we do for manual word alignments.

as we will see in the experimental results, the quality of a reordering model trained from automatic alignments is very sensitive to the quality of alignments.


The motivation for this is to explore if a good model can be learned even from a small amount of data if we restrict the number of features in a reasonable manner.

we see that even with only 2.4K sentences with manual word alignments our model benefits from lexical identities of more than the 1000 most frequent words.


These specifications were written for human programmers with no intention of providing support for automated processing.

when trained using the noisy supervision, our method achieves substantially more accurate translations than a state-of-the-art semantic parser (Clarke et al., 2010) (specifically, 80.0% in F-Score compared to an F-Score of 66.7%).


We use the technique described in Section 4.1 to determine the gender of a speaker of the current utterance.

with EM2010, this is not a hard constraint.


This renders our gender feature virtually useless, and results in lower accuracy on anaphoric speakers than on explicit speakers.

chekhov prefers to mention speaker names in the dialogues (46% of utterances are in the explicit-speaker category), which makes his prose slightly easier in terms of speaker identification.


Representing the probability of DOD as θ, after n occurrences of the verb the probability that y of them are DOD is: Considering that p(y| θ, n) is the likelihood in a Bayesian framework, the simplest and the most intuitive estimator of θ, given y in n verb occurrences, is the Maximum Likelihood Estimator (MLE): θ MLE is viable as a learning model in the sense that its accuracy increases as the amount of evidence for a verb grows (n → ∞), reflecting the incremental, on-line character of language learning.

one well known limitation of MLE is that it assigns zero probability mass to unseen events.


When learning begins, the prior probability is the only source of information for a learner and, as such, dominates the value of the posterior probability.

in the large sample limit, it is the likelihood that dominates the posterior distribution regardless of the prior.


For example, in 'John bought my car John sold my car' the inference is invalid due to an inherently incorrect rule, but the context is valid.

in 'my boss raised my salary my boss constructed my salary' the context {'my boss', 'my salary'} for applying 'raise → construct' is invalid.


Our first measure provides a nonparametric similarity by comparing the similarity of the rankings for intersection of the senses in both semantic signatures.

we additionally weight the similarity such that differences in the highest ranks are penalized more than differences in lower ranks.


(2009) and Hughes and Ramage (2007), which represent word meaning as the multinomials produced from random walks on the WordNet graph.

unlike our approach, neither of these disambiguates the two words being compared, which potentially conflates the meanings and lowers the similarity judgment.


", and person "A single human being; an individual" all align with someone n:1 (00007846-n).

two distinct definitions within the same Wiktionary entry should not map to the same WordNet sense.


Because we use a different approach, it would be possible to merge the two if the licenses allowed us to.

since the CC BY-SA and CC-BY-NC-SA licenses are mutually exclusive, the two works cannot be combined and rereleased unless relevant parties can relicense the works.


It can be downloaded to a local cluster, but the transfer cost is prohibitive at roughly 10 cents per gigabyte, making the total over $8000 for the full dataset.

3 it is unnecessary to obtain a copy of the data since it can be accessed freely from Amazon's Elastic Compute Cloud (EC2) or Elastic MapReduce (EMR) services.


This is different from running a single Map-Reduce job over the entire dataset, since websites in different subsets of the data cannot be matched.

since the data is stored as it is crawled, it is likely that matching websites will be found in the same split of the data.


As in McDonald (2006) and Clarke and Lapata (2008), our sequence-based compression model makes a binary "keep-or-delete" decision for each word in the sentence.

however, we Figure 1: Diagram of tree-based compression.


The current scorer Score Basic is still fairly naive in that it focuses only on features of the sentence to be compressed.

extra-sentential knowledge can also be important for queryfocused MDS.


(2006), which either uses minimal non-learning-based compression rules or is a pure extractive system.

our compression system sometimes generates less grammatical sentences, and those are mostly due to parsing errors.


MSA can thus find the best way to align the sequences with insertions or deletions in accordance with the scorer.

computing an optimal MSA is NP-complete (Wang and Jiang, 1994), thus we implement an approximate algorithm (Needleman and Wunsch, 1970) that iteratively aligns two sequences each time and treats the resulting alignment as a new sequence 2 .


A typical NLG system contains three main components: (1) Document (Macro) Planning -deciding what content should be realized in the output and how it should be structured; (2) Sentence (Micro) planninggenerating a detailed sentence specification and selecting appropriate referring expressions; and (3) Surface Realization -generating the final text after applying morphological modifications based on syntactic rules (see e.g., Bateman and Zock (2003), Reiter and Dale (2000) and McKeown (1985)).

document planning is arguably one of the most crucial components of an NLG system and is responsible for making the texts express the desired communicative goal in a coherent structure.


Typically, knowledge-based NLG systems are implemented by rules and, as mentioned above, have a pipelined architecture for the document and sentence planning stages and surface realization (Hovy, 1993;Moore and Paris, 1993).

document planning is arguably the most important task (Sripada et al., 2001).


(2013) where, in a given corpus, a combination of domain specific named entity tagging and clustering sentences (based on semantic predicates) were used to generate templates.

while the system consolidated both sentence planning and surface realization with this approach (described in more detail in Section 3), the document plan was given via the input data and sequencing information was present in training documents.


3 As indicated in Figure 2, the BLEU-4 scores are low for all DocSys and DocBase generations (as compared to DocOrig) for each domain.

the METEOR scores, while low overall (ranging from .201-.437) are noticeably increased over .


The biography domain illus- trates this scenario ( Figure 5) where the results are similar to the text-understandability experiments.

for weather domain, sentences from DocBase system were preferred to our system's ( Figure 6).


The bilingual lexicon can only express the translation equivalence between source-and tar-get-side word pair and has little ability to deal with word reordering and idiom translation.

phrase pair induction can make up for this deficiency to some extent.


It is obvious that, it is relatively high when using the News training data to evaluate the same News test set.

when the test domain is changed, the translation performance decreases to a large extent.


In tranductive learning, in-domain lexicon and both-side monolingual data have been explored.

this method does not take full advantage of both-side monolingual data because it uses source and target monolingual data individually.


in a text about finance, bank is likely to refer to a financial institution while in a text about geography, it is likely to refer to a river bank).

in the classic WSD task, ambiguous word types and a set of possible senses are known in advance.


Given monolingual text in a new domain, OOVs are easy to identify, and their translations can be acquired using dictionary extraction techniques (Rapp, 1995;Fung and Yee, 1998;Schafer and Yarowsky, 2002;Schafer, 2006;Haghighi et al., 2008;Mausam et al., 2010;Daumé III and Jagarlamudi, 2011), or active learning (Bloodgood and Callison-Burch, 2010).

previously seen (even frequent) words which require new translations are harder to spot.


If it were the case that only one sense existed for all word tokens of a particular type within a single domain, we would expect our word type features to be able to spot new senses without the help of the word token features.

in fact, even within a single domain, we find that often a word type is used with several senses, suggesting that word token features may also be useful.


The idea is that, if a word is used in one of the known senses then its context must have been seen previously and hence we hope that the PSD classifier outputs a spiky distribution.

if the word takes a new sense then hopefully it is used in an unseen context resulting in the PSD classifier outputting an uniform distribution.


We observe that words that are less frequent in both the old and the new domains are more likely to have a new sense than more frequent words, which causes the CONSTANT base-line to perform reasonably well.

it is more difficult for our models to make good predictions for less frequent words.


Again, our models perform better under a macro-level evaluation than under a micro-level evaluation.

in contrast to the SENSESPOTTING results, token-level features perform quite well on their own for all domains.


The knowledge engineering approach has been used in early grammatical error correction systems (Murata and Nagao, 1993;Bond et al., 1995;Bond and Ikehara, 1996;Heine, 1998).

as noted by (Han et al., 2006), rules usually have exceptions, and it is hard to utilize corpus statistics in handcrafted rules.


Suppose the corrected output of a system to be evaluated is exactly this perfectly corrected sentence The data is similar to the test set.

the ofcial HOO scorer using GNU Wdiff will automatically extract only one system edit with → to the for this system output.


The most direct comparison is SPIDER's F-score of 39.7% compared to his LSW03 algorithm's 35.6% (both are minimality resolvers).

our evaluation is more penalized since SPIDER loses precision for NER's false positives (Jack London as a location) while Leidner only evaluated on actual locations.


With regard to methodology, most of previous studies on argument extraction recast it as a Semantic Role Labeling (SRL) task and focus on intra-sentence information to identify the arguments and their roles.

argument extraction is much different from SRL in the sense that, while the relationship between a predicate and its arguments in SRL can be mainly decided from the syntactic structure, the relationship between an event trigger and its arguments are more semantics-based, especially in Chinese, as a paratactic (e.g., discourse-driven and pro-drop) language with the wide spread of ellipsis and the open flexible sentence structure.


We will use this gold information also in Section 6.1 to show that our system aligns well to the state of the art on the ACE 2004 benchmark.

in a realistic setting this information is not available or noisy.


Removing gold entity and mention information results in a significant F1 drop from 66.3% to 54.2%.

in a realistic setting we do not have gold entity info available, especially not in the case when we apply the system to any kind of text.


In the latter work we also tackled the problem of unsupervised phonemic prediction for unknown languages by using textual regularities of known languages.

we assumed that the target language was written in a known (Latin) alphabet, greatly reducing the difficulty of the prediction task.


This extra information may help with data sparsity, providing better estimates for rare and unseen n-grams.

there is still only modest overlap between the sentences for longer n-grams, particularly given that the corpus is sentencealigned and that 27% of the sentence pairs in this aligned data set are identical.


Because the data is aligned and therefore similar, we see the perplexity curves run parallel to each other as more data is added.

even though these sentences represent the same content, the language use is different between simple and normal and the normal data performs consistently worse.


to the GREC data sets, our RE candidates are not represented as the original surface strings, but as non-linearized subtrees.


When REG and linearization are applied on shallowSyn −re with gold shallow trees, the BLEU score is lower (60.57) as compared to the system that applies syntax and linearization on deepSyn +re , deep trees with gold REs (BLEU score of 63.9).

the BLEU r score, which generalizes over lexical RE mismatches, is higher for the REG→LIN components than for SYN→LIN.


The REG module benefits from the linearization in the case of deeplinSyn −re and prelinSyn −re , outperforming the component trained applied on the non-linearized deep syntax trees.

the REG module applied on prelinSyn −re , predicted shallow and linearized trees, clearly outperforms the module applied on deeplinSyn −re .


One of the most effective orthographic features is capitalization in English, which helps NER to generalize to new text of different genres.

capitalization is not very useful in some languages such as German, and nonexistent in other languages such as Arabic.


For example, Arabic allows the attachment of pronouns to nouns and verbs.

pronouns are rarely ever attached to named entities.


We also tested NER on the TWEETS test set, where we observed substantial improvements in recall (35.9%).

precision dropped by 6.2% for the reasons we mentioned earlier.


In this paper, we focus on providing a solution for social media text normalization as a preprocessing step for NLP applications.

this is a challenging problem for several reasons.


Traditionally, an OOV word is defined as a word that does not exist in the vocabulary of a given system.

this definition is not adequate for the social media text which has a very dynamic nature.


Our approach has some similarities with (Han et al., 2012) since both approaches utilize a normaliza-tion lexicon acquired form unlabeled data using distributional and string similarities.

our approach is significantly different since we acquire the lexicon using random walks on a contextual similarity graph which has a number of advantages over the pairwise similarity approach used in (Han et al., 2012).


The random walks will traverse more frequent paths; which would lead to more probable normalization equivalence.

the proposed approach provides high recall as well which is hard to achieve with higher precision.


Moreover, the lexicon contains some entries with foreign language words normalized to its English translation.

the lexicon has some bad normalization such as "'unrecycled "' which should be normalized to "'non recycled"' but since the system is limited to one word correction it did not get it.


To avoid unmodeled factors affecting the evaluation, we have carefully removed noise from our experiment datasets.

real Web pages are more complex; there are often instances of sentence fragments, such as captions and navigational link text.


These systems have the ability to handle questions with complex semantics on small domain-specific databases like GeoQuery (Tang and Mooney, 2001) or subsets of Freebase (Cai and Yates, 2013), but have yet to scale to the task of general, open-domain question answering.

our system answers questions with more limited semantics, but does so at a very large scale in an open-domain manner.


4 The RE-VERB database contains a large cross-section of general world-knowledge, and thus is a good testbed for developing an open-domain QA system.

the extractions are noisy, unnormalized (e.g., the strings obama, barack-obama, and president-obama all appear as distinct entities), and ambiguous (e.g., the relation born-in contains facts about both dates and locations).


Many of these problems and aid activities were reported via Twitter.

most problem reports and corresponding aid messages were not successfully exchanged between victims and local governments or humanitarian organizations, overwhelmed by the vast amount of information.


An evident alternative to this approach is to use sentiment analysis (Mandel et al., 2012;Tsagkalidou et al., 2011) assuming that problem reports should include something 'bad' while aid messages describe something 'good'.

we will show that this does not work well in our experiments.


They refer to events that activate troubles.

(B) is exemplified by ⟨school, X is collapsed⟩, ⟨battery, X runs out⟩, which imply that some non-trouble objects such as resources, appliances and facilities are dysfunctional.


They represent the dysfunction of troubles and can mean the solution or the settlement of troubles.

examples of (D) include ⟨school, X re-build⟩ and ⟨baby formula, buy X⟩.


Word Sentiment Polarity (WSP) As we suggested before, full-fledged sentiment analysis to recognize the expressions, including clauses and phrases, that refer to something good or bad was not effective in our task.

the sentiment polarity, assigned to single words turned out to be effective.


Most methods rely on local information (bag-of-words, short ngrams or elementary syntactic fragments) and do not attempt to account for more complex interactions.

these local lexical representations by themselves are often not sufficient to infer a sentiment or aspect for a fragment of text.


The clause let's not talk about the view could by itself be neutral or even positive given the right context (e.g., I've never seen such a fancy hotel room, my living room doesn't look that cool... and let's not talk about the view).

the contrast relation signaled by the connective but makes it clear that the second clause has a negative polarity.


Discourse cues typically do not directly indicate sentiment polarity (or aspect).

they can indicate how polarity (or aspect) changes as the text unfolds.


Note that we do not have a discourse relation SameSame since we do not expect to have strong linguistic evidence which states that an EDU contains the same sentiment information as the previous one.

4 we assume that the sentiment and topic flow is fairly smooth in general.


(2006), which jointly extracts opinion expressions, holders and their IS-FROM relations using an ILP approach.

our approach (1) also considers the IS-ABOUT relation which is arguably more complex due to the larger variety in the syntactic structure exhibited by opinion expressions and their targets, (2) handles implicit opinion relations (opinion expressions without any associated argument), and (3) uses a simpler ILP formulation.


Epistemological bias has not received much attention since it does not play a major role in overtly opinionated texts, the focus of much research on stance recognition.

our logistic regression model reveals that epistemological and other features can usefully augment the traditional sentiment and subjectivity features for addressing the difficult task of identifying the biasinducing word in a biased sentence.


We present a city navigation and tourist information mobile dialogue app with integrated question-answering (QA) and geographic information system (GIS) modules that helps pedestrian users to navigate in and learn about urban environments.

to existing mobile apps which treat these problems independently, our Android app addresses the problem of navigation and touristic questionanswering in an integrated fashion using a shared dialogue context.


Another user observation was that they did not have to stop to listen to information presented by our system (as it was in speech) and could carry on walking.

with the baseline system, they had to stop to read information off the screen.


Procedural dialog systems can help users achieve a wide range of goals.

such systems are challenging to build, currently requiring manual engineering of substantial domain-specific task knowledge and dialog management strategies.


Also related are studies on linguistic style accommodation (Mukherjee and Liu, 2012d) and user pair interactions (Mukherjee and Liu, 2013) in online debates.

these works do not consider tolerance analysis in debate discussions, which is the focus of this work.


They show that choices in the methodology, such as data sets, evaluation metrics and type of cross-validation can influence the conclusions of an experiment, as we also find in our second use case.

they focus on the problem of evaluation and recommendations on how to achieve consistent reproducible results.


In principle, it is to be expected that numbers are not exactly the same while evaluating against a different data set (the mc-set versus the rg-set), taking a different set of synsets to evaluate on (changing PoS-tag restrictions) or changing configuration settings that influence the similarity score.

a variation of up to 0.44 points in Spearman ρ and 0.60 in Kendall τ 6 leads to the question of how indicative these results really are.


This effect is not observed in wup and lch which correct for the depth of the hierarchy.

res, lin and jcn score better on the same set when verbs are considered, because they cannot detect any relatedness for the pair crane-implement when restricted to nouns.


The (fictional) Zigglebottom tagger is a tagger with spectacular results that looks like it will solve some major problems in your system.

the code is not available and a new implementation does not yield the same results.


Most of the research and evaluation considers query segmentation as a process analogous to identification of phrases within a query which when put within double-quotes (implying exact matching of the quoted phrase in the document) leads to better IR performance.

this is a very restricted view of the process and does not take into account the full potential of query segmentation.


2011have created larger annotated datasets through crowdsourcing 3 .

in their approach the crowd is provided with a few (four) possible segmentations of a query to choose from (known through a personal communication with a authors).


Similarly, Bharati et al (1995) defines it as Noun Group and Verb Group based only on local surface information.

cognitive and annotation experiments for chunking of English (Abney, 1992) and other language text (Bali et al., 2009) have shown that native speakers agree on major clause and phrase boundaries, but may not do so on more fine-grained chunks.


Honey pots or trap questions whose answers are known a priori are often included in a HIT to identify turkers who are unable to solve the task appropriately leading to incorrect annotations.

this trick cannot be employed in our case because there is no notion of an absolutely correct segmentation.


Various studies, including retrieving the accumulated question-answer pairs to find the related answer for a new question, finding the expert in a specific domain, summarizing single or multiple answers to provide a concise result, are conducted in the Community QA sites (Jeon et al., 2005;Adamic et al., 2008;Liu et al., 2008;Song et al., 2008;Si et al., 2010a;Figueroa and Atkinson, 2011).

an important issue which has been neglected so far is the detection of deceptive answers.


Some of the previous work (Song et al., 2010;Ishikawa et al., 2010;Bian et al., 2009) views the "best answer" as high quality answers, which are selected by the askers in the Community QA sites.

the deceptive answer may be selected as high-quality answer by the spammer, or because the general users are mislead.


Based on this assumption, they can extract the expert in the community, as discussed in Section 3.2.3.

we don't care which user is more knowledgeable, but are more interested in whether two users are both malicious users or authentic users.


The previous studies (Jeon et al., 2006;Song et al., 2010) find that the word features can't improve the performance on answer quality prediction.

from Table 1, we can see that the word features can provide some weak signals for deceptive answer prediction, for example, words "recommend", "address", "professional" express some kinds of promotion intent.


For answer quality prediction, length is an effective feature, for example, long-length provides very strong signals for high-quality answer (Shah and Pomerantz, 2010;Song et al., 2010).

for deceptive answer prediction, we find that the long answers are more potential to be deceptive.


Since this idea looks quite intuitive, many people would probably consider it as a solution to why-QA.

to our surprise, we could not find any previous work on why-QA that took this approach.


", although the "tsunamis" are modified by different predicates; "cause" and "generate."

effect part "tsunamis weaken as they pass through forests" of A4 implies that "tsunamis" are suppressed.


This approach was later improved by Wang and Manning (2010) with a tree-edit CRF model that learns the latent alignment structure.

general tree matching methods based on tree-edit distance have been first proposed by Punyakanok et al.


A recurrent neural network language model (Mikolov et al., 2010) aims to estimate the probability of observing a word given its preceding context.

one by-product of this model is the word embedding learned in its hidden-layer, which can be viewed as capturing the word meaning in some latent, conceptual space.


(2007) presented a generative probabilistic model based on a Quasi-synchronous Grammar formulation and was later improved by Wang and Manning (2010) with a tree-edit CRF model that learns the latent alignment structure.


Overall, by simply incorporating more information on word relations, we gain approximately 10 points in both MAP and MRR compared to surface-form matching (Line #4 vs. #2), consistently across all three models.

adding more information like named entity matching and answer type verification does not seem to help much (Line #5 vs. #4).


For example in Figure 1, "kindly" and "courteous" are incorrectly regarded as the modifiers for "foods" if the WAM is performed in an whole unsupervised framework.

by using some high-precision syntactic patterns, we can assert "courteous" should be aligned to "services", and "delicious" should be aligned to "foods".


As mentioned in (Liu et al., 2013), using PSWAM can not only inherit the advantages of WAM: effectively avoiding noises from syntactic parsing errors when dealing with informal texts, but also can improve the mining performance by using partial supervision.

is this kind of combination always useful for opinion target extraction?


it's not say that this combination is not useful.


Previous pattern learning algorithms (Zhuang et al., 2006;Kessler and Nicolov, 2009;Jijkoun et al., 2010) often extract opinion patterns by frequency.

some high-frequency syntactic patterns can have very poor precision (Kessler and Nicolov, 2009).


One straightforward option for Linear Programming formulation may seem like using the same Integer Linear Programming formulation introduced in §2.3, only changing the variable definitions to be real values ∈ [0, 1] rather than integers.

because the hard constraints in §2.3 are defined based on the assumption that all the variables are binary integers, those constraints are not as meaningful when considered for real numbers.


A very interesting work of Mohammad and Turney (2010) uses Mechanical Turk in order to build the lexicon of emotions evoked by words.

we present an automatic approach that infers the general connotation of words.


We presented an Egyptian to English MT system.

to previous work, we used an automatic conversion method to map Egyptian close to MSA.


Recent work (Zhao and Gildea, 2010) described an extension to the HMM with a fertility model, using MCMC techniques for parameter estimation.

they do not have a efficient means of MAP inference, which is necessary in many applications such as machine translation.


The structure of the fertility model violates the Markov assumptions used in this dynamic programming method.

we may empirically estimate the posterior distribution using Markov chain Monte Carlo methods such as Gibbs sampling (Zhao and Gildea, 2010).


1 Once Upon a Time... For years, the standard way to do statistical machine translation parameter tuning has been to use minimum error-rate training, or MERT (Och, 2003).

as researchers started using models with thousands of parameters, new scalable optimization algorithms such as MIRA (Watanabe et al., 2007;Chiang et al., 2008) and PRO (Hopkins and May, 2011) have emerged.


Ideally, the learning algorithm should be able to recover from overshooting.

once monsters are encountered, they quickly start dominating, with no chance for PRO to recover since it accumulates n-best lists, and thus also monsters, over iterations.


We can see in Table 3 that all selection mechanisms considerably improve BLEU compared to the baseline PRO, by 2-3 BLEU points.

not every selection alternative gets rid of monsters, which can be seen by the large lengths and low BLEU+1 for the negative examples (in bold).


Large variance between the results obtained with MIRA has also been reported (Simianer et al., 2012).

none of this work has focused on monsters.


For the first day ( ), the model functions the same as a standard DPM, i.e., all the topics use the same base measure, ( ).

for later days ( ), besides the base measure, ( ), we make use of topics learned from previous days as priors.


When available, using in-project training data proves significantly more successful than using out-of-project data.

we find that when using out-of-project data, a dataset based on more words than code performs consistently better.


as information source to produce or derive interpretations based on them.

existing uncertainty cues are ineffective in social media context because of its specific characteristics.


the language and scenario facilitate scalar implicature (Horn, 1972;Harnish, 1979;Gazdar, 1979).


show that their agents' linguistic behavior is broadly Gricean.

their agents' language is too simple to reveal implicatures.


We are not the first researchers to use lexicalized features for coreference resolution.

pre-  vious work has evaluated the benefit of lexical features only for broad-coverage data sets.


It is typically applicable in the text generation field, both for concept-to-text generation and text-totext generation (Lapata, 2003), such as multiple document summarization (MDS), question answering and so on.

ordering a set of sentences into a coherent text is still a hard and challenging problem for computers.


Nahnsen (2009) employed features which were based on discourse entities, shallow syntactic analysis, and temporal precedence relations retrieved from VerbOcean.

the model does not perform well on datasets describing the consequences of events.


Like sentence (3) and (4) in Figure 1, they have a highest cosine similarity of 0.2240 and most overlap words of "Israel" and "nuclear".

the similarity or overlap of the two sen-tences does not help to decide which sentence should be arranged before another.


This can be attributed to two factors: (i) the PCTB annotates non-local dependencies using traces, and (ii) Chinese syntax generates more traces than English syntax (Guo et al., 2007).

for parsers that do not return traces they are a benign error.


These methods usually rely heavily on the manually annotated treebanks for training the dependency models.

annotating syntac-  Figure 1: Different grammar formalisms of syntactic structures between CTB (upper) and CDT (below).


They also justified that the popular "early update" (Collins and Roark, 2004) is valid for the systems that do not exhibit spurious ambiguity 4 .

for the easy-first algorithm or more generally, systems that exhibit spurious ambiguity, even "early update" could fail to ensure validity of update (see the example in figure 1).


Work on opinion and sentiment tends to focus on explicit expressions of opinions.

many attitudes are conveyed implicitly, and benefactive/malefactive events are important for inferring implicit attitudes.


We report the results on 2011 only, because even when the same team participated in more than one year, the metrics submitted were different and the 2011 results represent the best effort of these teams.

as we saw in Table 1, in 2011 there were very few significant differences between the top summarization systems.


Because of its dependence on strings, it performs better with larger sets of model summaries.

to ROUGE, pyramid scoring is robust with as few as four or five model summaries (Nenkova and Passonneau, 2004).


The TDRM approach captures topics that are centered around a specific information need, often with a limited vocabulary, which favors word co-occurrence.

topics learned on entire collections are coarser than ours, which leads to lower coherence scores.


For that purpose, numerous procedures have been proposed (Milligan and Cooper, 1985).

none of the listed methods were effective or adaptable to our specific problem.


Ideally, each query subtopic should be represented by a unique cluster containing all the relevant Web pages inside.


First results are provided in Table 1 and evidence that the best configurations for different < p, K, S(W ik , W jl ) > tuples are obtained for high values of p, K ranging from 4 to 6 clusters and P M I steadily improving over SCP .

such a fuzzy configuration is not satisfactory.


Although supervised CWS models (Xue, 2003;Zhao et al., 2006;Zhang and Clark, 2007;Sun, 2011) proposed in the past years showed some reasonably accurate results, the outstanding problem is that they rely heavily on a large amount of labeled da-ta.

the production of segmented Chinese texts is time-consuming and expensive, since hand-labeling individual words and word boundaries is very hard (Jiao et al., 2006).


We note that when the number of texts changes from 0 to 50,000, the f-score and OOV both are improved.

when unlabeled data changes to 200,000, the performance is a bit decreased, while still better than not using unlabeled data.


These supervised methods show good results, however, are unable to incorporate information from new domain, where OOV problem is a big challenge for the research community.

unsupervised word segmentation Peng and Schuurmans (2001); Goldwater et al.


Their approach falls into an offline approach, which focuses on creating dictionaries by extracting new words from large corpora separately before WS.

offline approaches have limitation unless the lexicon is constantly updated.


This is why the result of +LM-S+LM-P is not shown for Chinese.

replacing LM-S with LM-P improved the performance significantly.


We found positive changes such as * 欧 麦/尔 萨 利 赫 oumai/ersalihe to 欧 麦 尔/萨 利 赫 oumaier/salihe "Umar Saleh" and * 领导/人 曼德拉 lingdao/renmandela to 领导人/曼德拉 lingdaoren/mandela"Leader Mandela".

considering the overall F-measure increase and proper noun F-measure decrease suggests that the effect of LM projection is not limited to proper nouns but also promoted finer granularity because we observed proper noun recall increase.


For the automated processing of spoken communication in these scenarios, a speech recognition system must be able to handle code switches.

the components of speech recognition systems are usually trained on monolingual data.


Furthermore, the integration of additional features as input is rather straightforward due to their structure.

factored language models (FLMs) have been used successfully for languages with rich morphology due to their ability to process syntactical features, such as word stems or part-of-speech tags (Bilmes and Kirchhoff, 2003;El-Desoky et al., 2010).


For the FLM, it leads to no improvement to add the language identifier as feature.

clustering the words into their languages on the output layer of the RNNLM leads to lower perplexities.


For all k and k , k-th column vector in WS are aligned k -th column vector in WT .

we can not measure similarity between the topic proportions because we do not have any language resources such as dictionary.


To mimimize the objective, gradient descend can be used.

but that is not convex function, we only obtained local optimal.


This is reasonable because we can assume that both languages share the same latent concept.

we cannot quantify the similarity between the topics because we do not have any external language resources such as a dictionary.


(2011) carefully explored review-related features based on content and sentiment, training a semi-supervised classifier for opinion spam detection.

the disadvantages of standard supervised learning methods are obvious.


Note that this is only a limitation of our inference procedure, not the model, and future work will look at other ways (e.g., Gibbs sampling) to perform inference.

generating Y and Z given X, such that the joke is funny, is still a formidable challenge that a lot of humans are not able to perform successfully (cf.


However, intermediate distances beyond the n-gram model limits can be very useful and should not be discarded.

distant-bigram models and distance-dependent trigger models make use of both, distance and co-occurrence, information up to window sizes of ten to twenty.


If this is not the case, then the text is likely to make little sense.

if this is the case, then the taboo meaning is potentially expanded to the phrase level.


Several other studies report results only on light verb constructions formed with certain light verbs (Stevenson et al., 2004;Tan et al., 2006;Tu and Roth, 2011).

we aimed to identify all kinds of LVCs, i.e.


In case of a large number of participating systems each assessor ranks only a subset of MT outputs.

a fair overall ranking cannot be always derived from such partial rankings (Callison-Burch et al., 2012).


(2010) proposed a methodology for classifying more details than was possible in the study by Sammons et al..

these studies were based on only English data sets.


The above studies rely on the high coverage of the original bilingual knowledge and a specific data source together with the translation vocabularies, co-occurrence information and language links.

the severest problem is that they cannot understand semantic information.


There is a large body of work around WSD and translation selection.

many of these approaches require lexical resources or large bilingual corpora with rich information fields and annotations, as reviewed in section 2.


However, aligned corpora can be difficult to obtain for under-resourced language pairs, and are expensive to construct.

documents in a comparable corpus comprise bilingual or multilingual text of a similar nature, and need not even be exact translations of each other.


For the precision metric, wiki-lsi scored 0.650 when all 80 input sentences are tested, while the base-freq baseline scored 0.550. goog-tr has the highest precision at 0.797.

if only the Chinese and Malay inputs -which has less presence on the Internet and 'less resource-rich' than English -were tested (since goog-tr cannot accept Iban inputs), wiki-lsi and goog-tr actually performs equally well at 0.690 precision.


An example is given in Figure 2, where the English word seems align to two Hindi words hE and lagawA.

from the small amount of labeled training data (i.e., a set of handcorrected tree pairs), we can learn what kind of source words are likely to align to multiple target words, and which target word is likely to the head.


Sign languages (SL), the vernaculars of deaf people, are complete, rich, standalone communication systems which have evolved in parallel with oral languages (Valli and Lucas, 2000).

in contrast to the last ones, research in automatic SL processing has not yet managed to build a complete, formal definition oriented to their automatic recognition (Cuxac and Dalle, 2007).


As the table shows, increasing the number of folds results in higher BLEU scores.

doing such will generally lead to higher variance among base learners.


Although the translation is correct in this situation, translating the Chinese word "lingyu" to "waters" appears very few times since the common translations are "areas" or "fields".

simply filtering out this kind of sentence pairs may lead to some loss of native English expressions, thereby the trans-lation performance is unstable since both nonparallel sentence pairs and non-literal but parallel sentence pairs are filtered.


Although the grammar may have rules to translate these two phrases, they can be safely pruned for this particular sentence pair.

to chart pruning for monolingual parsing, our pruning decisions are based on the source context, its target translation and the mapping between the two.


Moreover, for the frame "攻 占" in the input sentence, the MEANT-tuned system has correctly translated the ARG0 "哈玛斯好战 份子" into "Hamas militants" and the ARG1 "加 萨 走 廊" into "Gaza".

the TER-tuned system has dropped the predicate "施行" so that the corresponding arguments "The Palestinian Authority" and "into a state of emergency" have all been incorrectly associated with the predicate "攻 占 /seized".


In answer ranking phase, TI-QA considers the probabilities of different answer types as well: On one hand, TD-QA can achieve relative high ranking precision, as using a unique answer type greatly reduces the size of the candidate list for ranking.

as the answer-typing model is far from perfect, if prediction errors happen, TD-QA can no longer give correct answers at all.


However, as the answer-typing model is far from perfect, if prediction errors happen, TD-QA can no longer give correct answers at all.

tI-QA can provide higher answer coverage, as it can extract answer candidates with multiple answer types.


Since question-answer pairs are usually short, the word mismatching problem is especially important.

due to the lexical gap between questions and answers as well as spam typically existing in user-generated content, filtering and ranking answers is very challenging.


For example, they convert a 1, 000, 000-dimensional vector of word space into a 1000 × 1000 matrix.

in our model, a document is still represented by a vector.


For instance, in the British National Corpus, time and see are more frequent than thing or may and man is more frequent than part.

it seems intuitively right to say that time, see and man are more 'precise' concepts than thing, may and part respectively.


1 In Section 2, we defined semantic content as a notion encompassing various referential properties, including a basic concept of extension in cases where it is applicable.

we do not know of a dataset providing human judgements over the general informativeness of lexical items.


In biased-SVM, it is necessary to run SVM many times, as we searched "c" and "j".

mCDC does not require such parameter tuning.


Antonio Roque (2012) has challenged Ramsay's claim, and certainly there has been successful work done in the computational analysis and modeling of narratives, as we will review in the next section.

we believe that most previous work (except possibly (Elsner, 2012)) has failed to directly address the root cause of Ramsay's skepticism: can computers extract the emotions encoded in a narrative?


Not surprisingly, Claudius draws the most negative sentiment from Hamlet, receiving a score of -27.

gertrude is very well liked by Hamlet (+24), which is unexpected (at least to us) since Hamlet suspects that his mother was involved in murdering King Hamlet.


In the case of deep neural embeddings, for example, training time can number in days.

learned embeddings are becoming more abundant, as much research and computing effort is being invested in learning word representations using large-scale deep architectures trained on web-scale corpora.


min (φ iφj − φ i φ j ) 2 ∀i, j (where, with some abuse of notation, φ andφ are the source and target embeddings respectively).

this objective is no longer convex in the embeddings.


Adj + NP It is common practice to extract any NP modified by a sentiment adjective.

this simple extraction rule suffers from precision problems.


Quotes are used in news articles as evidence of a person's opinion, and thus are a useful target for opinion mining.

labelling each quote with a polarity score directed at a textually-anchored target can ignore the broader issue that the speaker is commenting on.


In sentiment analysis over product reviews, polarity labels are commonly used because the target, the product, is clearly identified.

for quotes on topics of debate, the target and meaning of polarity labels is less clear.


These approaches assume that each document has a single source (the document's author), whose communicative goal is to evaluate a well-defined target, such as a product or a movie.

this does not hold in news articles, where the goal of the journalist is to present the viewpoints of potentially many people.


Bag-of-words (BOW) is now the most popular way to model text in machine learning based sentiment classification.

the performance of such approach sometimes remains rather limited due to some fundamental deficiencies of the BOW model.


2012;Shi et al., 2010;Prettenhofer and Stein 2010).

the binary classification task is very different from the regression task studied in this paper, and the proposed methods in the above previous works cannot be directly applied.


REG_STC: It combines REG_S and REG_T by averaging their prediction values.

the above regression methods do not perform very well due to the unsatisfactory machine translation quality and the various language expressions.


Thus, each example in the unlabeled set is required to be checked by training a new regression model utilizing the example.

the model training process is usually very time-consuming for many regression algorithms, which significantly limits the use of the work in (Zhou and Li, 2005).


Suppose we would have sense tagged data, p(S|w, c) could have been computed as: But since the sense tagged corpus is not available, we cannot find #(S, w, c) from the corpus directly.

we can estimate it using the comparable corpus in other language.


Therefore, combining additional information with the LMs could reduce recognition errors.

direct integration of such information in the decoder is difficult.


An available treebank is a major resource for syntactic parsing.

it is often a key bottleneck to acquire credible treebanks.


Future systems might use the same or a similar feature set to ours, but in an architecture that does not include any generative parser.

some systems might indeed incorporate this generative model's score.


But, Φ phrase+deps+gen is significantly better than Φ phrase+gen only on F 1 , but not on UAS or LAS.

on the out-of-domain BROWN tests, we find that adding Φ deps always adds considerably, and in a statistically significant way, to both LAS and UAS.


This result is perhaps counter-intuitive, in the sense that one might have supposed that higher-order dependency features, being highly specific by nature, might only have only served to over-fit the training material.


While closed-form solutions have been developed for some specialized components (Martins et al., 2011), this problem is in general more difficult than the one arising in the subgradient algorithm.

the following result, proved in Martins et al.


We present an unsupervised approach to part-of-speech tagging based on projections of tags in a word-aligned bilingual parallel corpus.

to the existing state-of-the-art approach of Das and Petrov, we have developed a substantially simpler method by automatically identifying "good" training sentences from the parallel corpus and applying self-training.


(2012) build supervised POS taggers for 22 languages using the TNT tagger (Brants, 2000), with an average accuracy of 95.2%.

many widelyspoken languages -including Bengali, Javanese, and Lahnda -have little data manually labelled for POS, limiting supervised approaches to POS tagging for these languages.


Algorithm 2 describes this process of self training and revision, and assumes that the parallel source-target corpus has been word aligned, with many-to-one alignments removed, and that the sentences are sorted by alignment score.

to Algorithm 1, all sentences are used, not just the 60k sentences with the highest alignment scores.


Finally, the keywords S ⊆ t, with |S| ≤ k, are chosen by maximizing the cumulative reward function over all the topics, formulated as follows: Since R(S) is submodular, the greedy algorithm for maximizing R(S) is shown as Algorithm 1 on the next page, with r {w},z being similar to r S,z with S = {w}.

if λ = 1, the reward function is linear and only measures the topical similarity of words with the main topics of t. when 0 < λ < 1, as soon as a word is selected from a topic, other words from the same topic start having diminishing gains.


They find that these features only contribute less than 2% to precision.

in our approach linguistic features are quite useful.


Relatively low perplexity has made modified Kneser-Ney smoothing (Kneser and Ney, 1995;Chen and Goodman, 1998) a popular choice for language modeling.

existing estimation methods require either large amounts of RAM (Stolcke, 2002) or machines (Brants et al., 2007).


We have found that, even with mostlysequential access, memory mapping is slower because the kernel does not explicitly know where to read ahead or write behind.

we use dedicated threads for reading and writing.


We note that interpolation (Equation 2) used the different backoff b(w n−1 1 ) and so b(w n 1 ) is not immediately available.

the backoff values were saved in suffix order ( §3.3) and interpolation produces probabilities in suffix order.


To avoid the need for hard decisions about domain membership, some have used topic modeling to improve SMT performance, e.g., using latent semantic analysis (Tam et al., 2007) or 'biTAM' (Zhao and Xing, 2006).

to our source language approach, these authors use both source and target information.


For instance, in the task of tagging mail addresses, a feature of "5 consecutive digits" is highly indicative of a POSTCODE.

in the alignment model, it does not make sense to design features based on a hard-coded state, say, a feature of "source word lemma matching target word lemma" fires for state index 6.


While the participants in that study were younger (7 to 9+ years old), the study is relevant because the challenges those young participants face, are faced again when readers of any age encounter new and complicated texts that present words they do not know, and ideas they have never considered.

there is ample work on the basic algorithm to place a sequence of words in a typesetting area with a certain width, commonly known as the optimal line breaking problem (e.g., , Knuth and Plass (1981)).


It should be noted that when a text is typeset into an area of width of a certain number of characters, an erroneous break need not necessarily lead to an actual break in the final output, that is an error may not be too bad.

a missed break while not hurting the readability of the text may actually lead to a long segment that may eventually worsen raggedness in the final typesetting.


Each example has more than one annotation and we need to determine a single sense tag for each example.

if we assign senses by majority voting, we need a backoff strategy in case of ties.


Columns V and B show respectively whether the underspecified senses are a result of majority voting or backoff.

to volunteers, turkers disprefer the underspecified option and most of the English underspecified senses are assigned by backoff.


Syntax-based vector spaces are used widely in lexical semantics and are more versatile than word-based spaces (Baroni and Lenci, 2010).

they are also sparse, with resulting reliability and coverage problems.


The sentence Karen threw her arms round my neck, spilling champagne everywhere contains the LU throw.v evoking the frame BODY MOVEMENT.

throw.v is ambiguous and may also evoke CAUSE MOTION.


The evaluation of whole-sentence semantic structures plays an important role in semantic parsing and large-scale semantic structure annotation.

there is no widely-used metric to evaluate wholesentence semantic structures.


Evaluating such structures is necessary for semantic parsing tasks, as well as semantic annotation tasks which create linguistic resources for semantic parsing.

there is no widely-used evaluation method for whole-sentence semantic structures.


The smatch score is the maximum of the f-scores.

for AMRs that contain large number of variables, it is not efficient to get the f-score by simply using the method above.


We present a pilot evaluation DM.HR on a standard task from distributional semantics, namely synonym choice.

to tasks like predicting word similarity We use the dataset created by Karan et al.


To identify news stories on the same topic, most TDT approaches rely on traditional vector space models (Salton et al., 1975), as more sophisticated natural language processing techniques have not yet proven to be useful for this task.

significant advances in sentence-level event extraction have been made over the last decade, in particular as the result of standardization efforts such as TimeML (Pustejovsky et al., 2003a) and TimeBank (Pustejovsky et al., 2003b), as well as dedicated evaluation tasks (ACE, 2005;Verhagen et al., 2007;Verhagen et al., 2010).


The VSM is at the core of most approaches that identify sametopic news stories (Hatzivassiloglou et al., 2000;Brants et al., 2003;Kumaran and Allan, 2005;Atkinson and Van der Goot, 2009).

it has been observed that some word classes (e.g., named entities, noun phrases, collocations) have more significance than the others.


When considering average precision, all kernel models significantly (at p < 0.01) outperform the baseline.

when considering R-precision, only the conormal PGK model significantly (at p < 0.05) outperforms the baseline.


In contrast, the system produced precision of 0.96 when ambiguity detection was employed.

the inclusion of disambiguation did reduce the overall recall; the system that employed disambiguation returned only about 57% of the true positives returned by the system that did not employ disambiguation.


Finally a multi-class classifier is trained.


(2012) design a generative model to identify noise patterns.

as shown in the experiments (Section 4), the above variants do not lead to much improvement in accuracy.


To reduce the number of false negative examples, we propose a new method to construct negative examples by utilizing the 1-to-1/1-to-n/n-to-1/n-to-n property of a relation.

1-to-1/n-to-1/1-to-n Relation A 1-to-1 or n-to-1 relation is a functional relation: for a relation r, for each valid source entity e 1 , there is only one unique destination entity e 2 such that (e 1 , e 2 ) ∈ r. in a real KB like Freebase, very few relations meet the exact criterion.


Recall that in our second baseline, we employ ACs to postprocess the output of the stance classifier simply by summing up the confidence values assigned to the posts written by the same author for the same debate domain.

since we now want to enforce two types of inter-post constraints (namely, ACs and ICs), we will have to employ a more sophisticated inference mechanism.


Previous work has focused on employing graph minimum cut (MinCut) as the inference algorithm.

since MinCut suffers from the weakness of not being able to enforce negative constraints (i.e., two posts cannot receive the same label) (Bansal et al., 2008), we propose to use integer linear programming (ILP) as the underlying inference mechanism.


What's more these works all model on a word level.

it is very useful to regard sentence as the basic processing unit, for example in the text scanning approach simulating human reading process by Xu and Zhuge (2013).


In our task, we provide a set of potential events.

most of the candidate events won't have ever been reported within a user's posting history.


Finally, since correlations between the textual features and non-textual features are highly non-linear, concatenating these features simply sometimes can submerge classification performance.

mDBN enjoys the advantage of the shared repre-sentation between textual features and non-textual features using the deep learning architecture.


In (Hassan and Radev, 2010) and (Hassan et al., 2011), a Markov random walk model is applied to a large word relatedness graph, constructed according to the synonyms and hypernyms in WordNet (Miller, 1995).

approaches based on seed words has obvious shortcomings.


Tree kernels were inspired in part by ideas from Data-Oriented Parsing (Scha, 1990;Bod, 1993), which was in turn motivated by uncertainty about which fragments to include in a grammar.

manual and automatic approaches to inducing tree fragments have recently been found to be useful in an explicit approach to text classification, which employs specific tree fragments as features in standard classifiers (Post, 2011;Wong and Dras, 2011;Swanson and Charniak, 2012).


1996;van de Weijer 1998) Table 1: Diphone behavior rich source of information for word segmentation: obstruent-initial diphones are generally informative as to the presence/absence of word boundaries.

as we suspected, vowelvowel sequences are problematic, since they occur freely both within words and across word boundaries.


Van Petten and Luka (2012) argue that word expectations that are confirmed result in reduced N400 size, whereas expectations that are disconfirmed increase the PNP.

in a probabilistic setting, expectations are not all-or-nothing so there is no strict distinction between confirmation and disconfirmation.


This has the advantage that our findings are likely to generalise to other sentence stimuli, but it can also raise a possible concern: The N400 effect may not be due to surprisal itself, but to an unknown confounding variable that was not included in the regression analysis.

this seems unlikely because of two additional findings that only follow naturally if surprisal is indeed the relevant predictor: Significant results only appeared where they were most expected a priori (i.e., on N400 but not on other components) and there was a nearly monotonic relation between the models' word-prediction accuracy and their ability to account for N400 size.


Ensemble methods are widely used in machine learning and have been shown to be often very effective (Breiman, 1996;Smyth and Wolpert, 1999;MacKay, 1991;Freund et al., 2004).

ensemble methods and their theory have been developed primarily for binary classification or regression tasks.


As the bottom-up Eisner Algorithm must maintain the nested structural constraint, it cannot parse the non-projective dependency trees like 8' and 9' in Figure 2.

the non-projective dependency does exist in real discourse.


A majority of researches regard discourse parsing as a classification task and mainly focus on exploiting various linguistic features and classifiers when using PDTB (Wellner et al., 2006;Pitler et al., 2009;Wang et al., 2010).

the predicatearguments annotation scheme itself has such a limitation that one can only obtain the local discourse relations without knowing the rich context.


As expected, the MT system slightly outperforms our models on most language pairs.

the overall performance of the models is comparable to that of the MT system.


Because our crawling algorithm so closely models the guidelines, this puts our system in an interesting position to provide feedback to the Shared Task organizers.

the close match between our crawling algorithm and the annotation guidelines supported by the mapping to MRS provides for very high precision and recall when the analysis engine produces the desired MRS. 7 the analysis engine does not always provide the desired analysis, largely because of idiosyncrasies of the genre (e.g.


Being rule-based, our system does not require any training data per se.

the majority of our rule development and error analysis were performed against the designated training data.


It lowers precisions in RTE2 and RTE3 data, particularly in "IE" subtask (where precisions drop under 0.5).


Furthermore, this method can only account for a very small part of phrases, since most of the phrases are compositional.

our method attempts to learn the semantic vector representation for any phrase.


Most of them also assume that the input must be in document level.

this situation does not always happen since there is considerable amount of parallel data which does not have document boundaries.


Analysing and extracting useful information from the web has become an increasingly important research direction for the NLP community, where many tasks require part-of-speech (POS) tagging as a fundamental preprocessing step.

state-of-the-art POS taggers in the literature (Collins, 2002;Shen et al., 2007) are mainly optimized on the the Penn Treebank (PTB), and when shifted to web data, tagging accuracies drop significantly (Petrov and McDonald, 2012).


We can see that using the target domain data achieves similar improvements compared with using the mixed data.

for the email domain, RBM-W yields much smaller improvement compared with RBM-E, and vice versa.


Being specific to Apple forums, we did not use them for initialization in experiments so far with the intent of keeping the technique generic.

when such posts are initialized as solutions (in addition to first replies as we did earlier), the F-score for solution identification for our technique was seen to improve slightly, to 64.5% (from 64%).


As for unigram features, not surprisingly, "rt" and "retweet" are top features for both our approach and TAC+ff+time.

the other unigrams for the two methods seem to be a bit different in spirit.


The convergence rate for the average posterior probability estimates P µ (R|T ) depending on the number of tweets is similar to the user model results presented in Figure 6.

for G geo the variance for P µ (R|T ) is higher for Democratic users; for G ZLR P µ (R|T ) → 1 for Republicans in less than 110 tweets which is ∆t = 40 tweets faster than the user model; for G cand the convergence for both P µ (R|T ) → 1 and P µ (D|T ) → 0 is not significantly different than the user model.


Much of the recent work on dependency parsing has been focused on solving inherent combinatorial problems associated with rich scoring functions.

we demonstrate that highly expressive scoring functions can be used with substantially simpler inference procedures.


Note that Equation 4 contains exponentially many constraints and cannot be enforced jointly for general scoring functions.

our sampling procedure generates a small number of structures along the search path.


Generally, this feature can be defined based on an instance of grandparent structure.

we also handle the case of coordination.


We apply the Random Walk-based sampling method (see Section 3.2.2) for the standard dependency parsing task.

for the joint parsing and POS correction on the CATiB dataset we do not use the Random Walk method because the first-order features in normal parsing are no longer first-order when POS tags are also variables.


On the CATiB dataset, we restrict the sample trees to always be projective as described in Section 3.2.1.

we do not impose this constraint for the CoNLL datasets.


Given sufficient time, both sampling methods achieve the same score.

the Random Walk-based sampler performs better when the quality is traded for speed.


Without the coarse pass, the dense marginal computation is not efficient on a GPU, processing only 32 sentences per second.

our approach allows us to process over 190 sentences per second, almost a 6x speedup.


the code itself), which allows the compiler to more effectively use all of its registers.


As Petrov and Klein (2007) have shown, intermediate-sized Berkeley grammars prune many more symbols than the X-bar system.

they are slower to parse with in a CPU context, and so they begin with an X-bar grammar.


The Viterbi algorithm is a reasonably effective method for parsing.

many authors have noted that parsers benefit substantially from minimum Bayes risk decoding (Goodman, 1996;Simaan, 2003;Matsuzaki et al., 2005;Titov and Henderson, 2006;Petrov and Klein, 2007).


By itself, this approach works on nearly every sentence.

scores for approximately 0.5% of sentences overflow (sic).


For any subsequent SHIFT action (SHIFT, c) to be valid, the necessary condition is c ≡ c lex 0 , where c lex 0 denotes the gold-standard lexical category of the front word in the queue, q 0 (line 3).

this condition is not sufficient; a counterexample is the case where all the goldstandard lexical categories for the sentence in Figure 2 are shifted in succession.


(2013d) compare their predict models to "Latent Semantic Analysis" (LSA) count vectors on syntactic and semantic analogy tasks, finding that the predict models are highly superior.

they provide very little details about the LSA count vectors they use.


Count models have such a long and rich history that we can only explore a small subset of the counting, weighting and compressing methods proposed in the literature.

it is worth pointing out that the evaluated parameter subset encompasses settings (narrow context window, positive PMI, SVD reduction) that have been found to be most effective in the systematic explorations of the parameter space conducted by Bullinaria and Levy (2007;.


Instead, we found that the predict models are so good that, while the triumphalist overtones still sound excessive, there are very good reasons to switch to the new architecture.

due to space limitations we have only focused here on quantitative measures: It remains to be seen whether the two types of models are complementary in the errors they make, in which case combined models could be an interesting avenue for further work.


results in performance which is almost 70% of ALLEQ, demonstrating the value of weakly supervised data.

5EQ, which cannot use this weak supervision, performs much worse.


These systems are effective because researchers can incorporate a large body of handcrafted features into the models.

the ability of these models is restricted by the design of features and the number of features could be so large that the result models are too large for practical use and prone to overfit on training corpus.


In addition, (Hai et al., 2012) extracted opinion targets/words in a bootstrapping process, which had an error propagation problem.

we perform extraction with a global graph co-ranking process, where error propagation can be effectively alleviated.


Semi-supervised techniques have been proposed for sentence-level sentiment classification (Täckström and McDonald, 2011a;Qu et al., 2012).

they rely on a large amount of document-level sentiment labels that may not be naturally available in many domains.


Most previous work using PR mainly experiments with featurelabel constraints.

we explore a rich set of linguistically-motivated constraints which cannot be naturally formulated in the feature-label form.


CRF-INF disc slightly outperforms CRF but the improvement is not significant.

both PR lex and PR significantly outperform CRF, which implies that incorporating lexical and discourse constraints as posterior constraints is much more effective.


This is because it over-predicts the polar sentences in the polar documents, and predicts no polar sentences in the neutral documents.

our PR models provide more balanced F1 scores among all the sentiment categories.


A simple lexicon-based constraint during inference time may also correct this case.

hard-constraint baselines can hardly improve the performance in general because the contributions of different constraints are not learned and their combination may not lead to better predictions.


The second example in Table 5 shows that the PR model learned with discourse constraints correctly predicts the sentiment of two sentences where no lexical constraints apply.

discourse constraints are not always helpful.


Previous works often use syntax constituents in this task.

syntax-based methods can only use discrete contextual information, which may suffer from data sparsity.


A recent research (Xu et al., 2013) extracted infrequent product features by a semi-supervised classifier, which used word-syntactic pattern co-occurrence statistics as features for the classifier.

this kind of feature is still sparse for infrequent candidates.


In conventional neural models, the candidate term t is placed in the center of the window.

from Example 2, when l = 5, we can see that the best windows should be the bracketed texts (Because, intuitively, the windows should contain mp3, which is a strong evidence for finding the product feature), where t = {screen} is at the boundary.


We believe the reason that LEX or CONT is better is that syntactic patterns only use discrete and local information.

cONT exploits latent semantics of each word in context, and LEX takes advantage of word embedding, which is induced from global word co-occurrence statistic.


As for SGW-TSVM, the features they used for the TSVM suffer from the data sparsity problem for infrequent terms.

lEX&CONT is frequency-independent to the review corpus.


Topic modeling is a popular method for the task.

unsupervised topic models often generate incoherent aspects.


In (Yang et al., 2011), a user provided parameter indicating the technicality degree of a domain was used to model the language gap between topics.

our method is fully automatic without human intervention.


The terms in each document are assumed to be generated by first sampling a topic z, and then a cluster c given topic z, and finally a term w given topic z and cluster c. This plate notation of AKL and its associated generative process are similar to those of MC-LDA (Chen et al., 2013b).


Traditionally, topic models have been evaluated using perplexity.

perplexity on the heldout test set does not reflect the semantic coherence of topics and may be contrary to human judgments (Chang et al., 2009).


Spectral methods offer scalable alternatives to Markov chain Monte Carlo and expectation maximization.

these new methods lack the rich priors associated with probabilistic models.


(2012a) chose the C that minimizes the KL divergence betweenQ i,• and the reconstruction based on the anchor word's conditional word vec- (3) The anchor method is fast, as it only depends on the size of the vocabulary once the cooccurrence statistics Q are obtained.

it does not support rich priors for topic models, while MCMC (Griffiths and Steyvers, 2004) and variational EM (Blei et al., 2003) methods can.


This paper introduces two different regulariza-tions that offer users more interpretable models and the ability to inject prior knowledge without sacrificing the speed and generalizability of the underlying approach.

one sacrifice that this approach does make is the beautiful theoretical guarantees of previous work.


The latter have been applied specifically to the problem of estimating word probabilities with sparse additive generative (SAGE) models (Eisenstein et al., 2011), where sparse extra-linguistic effects can influence a word probability in a larger generative setting.

to previous work in which the probability of a word linked to a character is dependent entirely on the character's latent persona, in our model, we see the probability of a word as dependent on: (i) the background likelihood of the word, (ii) the author, so that a word becomes more probable if a particular author tends to use it more, and (iii) the character's persona, so that a word is more probable if appearing with a particular persona.


Then we define the weight matrix representing the coreferential relation as: if m i and m j are coreferential, and c i = c j 0 Otherwise Ensuring topical coherence (Principle 3) has been beneficial for wikification on formal texts (e.g., News) by linking a set of semantically-related mentions to a set of semantically-related concepts simultaneously Ratinov et al., 2011;Cheng and Roth, 2013).

the shortness of a single tweet means that it may not provide enough topical clues.


We can easily see that the system performce is stable when µ < 0.4.

when µ ≥ 0.4, the system performance dramatically decreases, showing that prior popularity is not enough for an end-toend wikification system.


The text of any DOM tree node that is shorter than 140 characters is a candidate entity.

without further restrictions, the number of possible entity lists grows exponentially with the number of candidate entities.


In the literature, many information extraction systems employ more versatile extraction predicates (Wang and Cohen, 2009;Fumarola et al., 2011).

despite the simplicity, we are able to find an extraction predicate that extracts a compatible entity list in 69.7% of the development examples.


For instance, entities in many categories (e.g., people and place names) usually have only 2-3 word tokens, most of which are proper nouns.

random words on the web page tend to have more diverse lengths and part-of-speech tags.


Since zero-shot entity extraction is a new task, we cannot directly compare our system with other systems.

we can mimic the settings of other tasks.


Our work shares a base with the wrapper induction literature (Kushmerick, 1997) in that it leverages regularities of web page structures.

wrapper induction usually focuses on a small set of web domains, where the web pages in each domain follow a fixed template (Muslea et al., 2001;Crescenzi et al., 2001;Cohen et al., 2002;Arasu and Garcia-Molina, 2003).


In recent years, there has been a drive to scale semantic parsing to large databases such as Freebase (Cai and Yates, 2013;Berant et al., 2013;Kwiatkowski et al., 2013).

despite the best efforts of information extraction, such databases will always lag behind the open web.


For example, a PER mention is unlikely to have more than one employer.

a GPE mention can be a physical location for multiple entity mentions.


All these work noted the advantage of exploiting crosscomponent interactions and richer knowledge.

they relied on models separately learned for each subtask.


Using parse accuracy in a simple reranking strategy for selfmonitoring, we find that with a stateof-the-art averaged perceptron realization ranking model, BLEU scores cannot be improved with any of the well-known Treebank parsers we tested, since these parsers too often make errors that human readers would be unlikely to make.

by using an SVM ranker to combine the realizer's model score together with features from multiple parsers, including ones designed to make the ranker more robust to parsing mistakes, we show that significant increases in BLEU scores can be achieved.


Simple ranking with the Berkeley parser of the generative model's n-best realizations raised the BLEU score from 85.55 to 86.07, well below the averaged perceptron model's BLEU score of 87.93.

as shown in Table 2, none of the parsers yielded significant improvements on the top of the perceptron model.


As with the base grammar, missing grammar entries are guessed from the expanded grammar.

we do this only in cases where a correct grammar entry cannot be guessed from the base grammar.


Conceptually, this conversion is similar to the conversions from CTB structures to representations in deep grammar formalisms (Tse and Curran, 2010;Yu et al., 2010;Guo et al., 2007;Xia, 2001).

our work is grounded in GB, which is the linguistic basis of the construction of CTB.


Supervised dependency parsing has made great progress during the past decade.

it is very difficult to further improve performance * Correspondence author of supervised parsers.


To alleviate the noise, the tri-training method only uses unlabeled data on which multiple parsers from different views produce identical parse trees.

unlabeled data with divergent syntactic structures should be more useful.